


import argparse

import torch
from datasets import load_dataset
from donut import DonutModel

from transformers import (
    DonutImageProcessor,
    DonutProcessor,
    DonutSwinConfig,
    DonutSwinModel,
    MBartConfig,
    MBartForCausalLM,
    VisionEncoderDecoderModel,
    XLMRobertaTokenizerFast,
)




def       __lowercase						(    _UpperCamelCase  )     ->List[Any]:







      """simple docstring"""

      lowercase      : Dict        =		model.config

      lowercase      : int        =		DonutSwinConfig(
          image_size=original_config.input_size,     patch_size=4,     depths=original_config.encoder_layer,     num_heads=[4, 8, 16, 32],     window_size=original_config.window_size,     embed_dim=128,     )
      lowercase      : str        =		MBartConfig(
          is_decoder=UpperCamelCase_,     is_encoder_decoder=UpperCamelCase_,     add_cross_attention=UpperCamelCase_,     decoder_layers=original_config.decoder_layer,     max_position_embeddings=original_config.max_position_embeddings,     vocab_size=len(
              model.decoder.tokenizer  ),     scale_embedding=UpperCamelCase_,     add_final_layer_norm=UpperCamelCase_,     )

      return encoder_config, decoder_config




def       __lowercase						(    _UpperCamelCase  )     ->int:







      """simple docstring"""

      if "encoder.model" in name:
            lowercase      : Optional[int]        =		name.replace('''encoder.model''',     '''encoder'''  )
      if "decoder.model" in name:
            lowercase      : Dict        =		name.replace('''decoder.model''',     '''decoder'''  )
      if "patch_embed.proj" in name:
            lowercase      : str        =		name.replace('''patch_embed.proj''',     '''embeddings.patch_embeddings.projection'''  )
      if "patch_embed.norm" in name:
            lowercase      : Dict        =		name.replace('''patch_embed.norm''',     '''embeddings.norm'''  )
      if name.startswith('''encoder'''  ):
            if "layers" in name:
                  lowercase      : Dict        =		'''encoder.''' + name
            if "attn.proj" in name:
                  lowercase      : Any        =		name.replace('''attn.proj''',     '''attention.output.dense'''  )
            if "attn" in name and "mask" not in name:
                  lowercase      : str        =		name.replace('''attn''',     '''attention.self'''  )
            if "norm1" in name:
                  lowercase      : Dict        =		name.replace('''norm1''',     '''layernorm_before'''  )
            if "norm2" in name:
                  lowercase      : Union[str, Any]        =		name.replace('''norm2''',     '''layernorm_after'''  )
            if "mlp.fc1" in name:
                  lowercase      : Tuple        =		name.replace('''mlp.fc1''',     '''intermediate.dense'''  )
            if "mlp.fc2" in name:
                  lowercase      : str        =		name.replace('''mlp.fc2''',     '''output.dense'''  )

            if name == "encoder.norm.weight":
                  lowercase      : Dict        =		'''encoder.layernorm.weight'''
            if name == "encoder.norm.bias":
                  lowercase      : Tuple        =		'''encoder.layernorm.bias'''

      return name




def       __lowercase						(    _UpperCamelCase,     _UpperCamelCase  )     ->Optional[int]:







      """simple docstring"""

      for key in orig_state_dict.copy().keys():
            lowercase      : Optional[int]        =		orig_state_dict.pop(UpperCamelCase_  )

            if "qkv" in key:
                  lowercase      : int        =		key.split('''.'''  )
                  lowercase      : str        =		int(key_split[3]  )
                  lowercase      : Optional[Any]        =		int(key_split[5]  )
                  lowercase      : Tuple        =		model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

                  if "weight" in key:
                        lowercase      : Dict        =		val[:dim, :]
                        lowercase      : int        =		val[dim : dim * 2, :]
                        lowercase      : Any        =		val[-dim:, :]
                  else:
                        lowercase      : Optional[int]        =		val[:dim]
                        lowercase      : Tuple        =		val[dim : dim * 2]
                        lowercase      : int        =		val[-dim:]
            elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
                  # HuggingFace implementation doesn't use attn_mask buffer
                  # and model doesn't use final LayerNorms for the encoder
                  pass
            else:
                  lowercase      : Optional[Any]        =		val

      return orig_state_dict




def       __lowercase						(    _UpperCamelCase,     _UpperCamelCase=None,     _UpperCamelCase=False  )     ->Dict:







      """simple docstring"""

      lowercase      : List[str]        =		DonutModel.from_pretrained(UpperCamelCase_  ).eval()

      # load HuggingFace model
      lowercase ,      lowercase      : List[Any]        =		get_configs(UpperCamelCase_  )
      lowercase      : int        =		DonutSwinModel(UpperCamelCase_  )
      lowercase      : Optional[Any]        =		MBartForCausalLM(UpperCamelCase_  )
      lowercase      : Optional[int]        =		VisionEncoderDecoderModel(encoder=UpperCamelCase_,     decoder=UpperCamelCase_  )
      model.eval()

      lowercase      : str        =		original_model.state_dict()
      lowercase      : str        =		convert_state_dict(UpperCamelCase_,     UpperCamelCase_  )
      model.load_state_dict(UpperCamelCase_  )

      # verify results on scanned document
      lowercase      : int        =		load_dataset('''hf-internal-testing/example-documents'''  )
      lowercase      : Union[str, Any]        =		dataset['''test'''][0]['''image'''].convert('''RGB'''  )

      lowercase      : str        =		XLMRobertaTokenizerFast.from_pretrained(UpperCamelCase_,     from_slow=UpperCamelCase_  )
      lowercase      : Dict        =		DonutImageProcessor(
          do_align_long_axis=original_model.config.align_long_axis,     size=original_model.config.input_size[::-1]  )
      lowercase      : List[str]        =		DonutProcessor(UpperCamelCase_,     UpperCamelCase_  )
      lowercase      : Tuple        =		processor(UpperCamelCase_,     return_tensors='''pt'''  ).pixel_values

      if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
            lowercase      : Optional[Any]        =		'''<s_docvqa><s_question>{user_input}</s_question><s_answer>'''
            lowercase      : Optional[int]        =		'''When is the coffee break?'''
            lowercase      : Tuple        =		task_prompt.replace('''{user_input}''',     UpperCamelCase_  )
      elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
            lowercase      : Union[str, Any]        =		'''<s_rvlcdip>'''
      elif model_name in [
          "naver-clova-ix/donut-base-finetuned-cord-v1",
          "naver-clova-ix/donut-base-finetuned-cord-v1-2560",
      ]:
            lowercase      : Union[str, Any]        =		'''<s_cord>'''
      elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
            lowercase      : int        =		'''s_cord-v2>'''
      elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
            lowercase      : int        =		'''<s_zhtrainticket>'''
      elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
            # use a random prompt
            lowercase      : List[str]        =		'''hello world'''
      else:
            raise ValueError('''Model name not supported'''  )
      lowercase      : Optional[Any]        =		original_model.decoder.tokenizer(UpperCamelCase_,     add_special_tokens=UpperCamelCase_,     return_tensors='''pt'''  )[
          '''input_ids'''
      ]

      lowercase      : Tuple        =		original_model.encoder.model.patch_embed(UpperCamelCase_  )
      lowercase ,      lowercase      : Tuple        =		model.encoder.embeddings(UpperCamelCase_  )
      assert torch.allclose(UpperCamelCase_,     UpperCamelCase_,     atol=1e-3  )

      # verify encoder hidden states
      lowercase      : int        =		original_model.encoder(UpperCamelCase_  )
      lowercase      : Optional[Any]        =		model.encoder(UpperCamelCase_  ).last_hidden_state
      assert torch.allclose(UpperCamelCase_,     UpperCamelCase_,     atol=1e-2  )

      # verify decoder hidden states
      lowercase      : Tuple        =		original_model(UpperCamelCase_,     UpperCamelCase_,     UpperCamelCase_  ).logits
      lowercase      : str        =		model(UpperCamelCase_,     decoder_input_ids=UpperCamelCase_  ).logits
      assert torch.allclose(UpperCamelCase_,     UpperCamelCase_,     atol=1e-3  )
      print('''Looks ok!'''  )

      if pytorch_dump_folder_path is not None:
            print(f"""Saving model and processor to {pytorch_dump_folder_path}"""  )
            model.save_pretrained(UpperCamelCase_  )
            processor.save_pretrained(UpperCamelCase_  )

      if push_to_hub:
            model.push_to_hub('''nielsr/''' + model_name.split('''/'''  )[-1],     commit_message='''Update model'''  )
            processor.push_to_hub('''nielsr/''' + model_name.split('''/'''  )[-1],     commit_message='''Update model'''  )


if __name__ == "__main__":
  __a 					=   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--model_name''',
      default='''naver-clova-ix/donut-base-finetuned-docvqa''',
      required=False,
      type=str,
      help='''Name of the original model you\'d like to convert.''',
  )
  parser.add_argument(
      '''--pytorch_dump_folder_path''',
      default=None,
      required=False,
      type=str,
      help='''Path to the output PyTorch model directory.''',
  )
  parser.add_argument(
      '''--push_to_hub''',
      action='''store_true''',
      help='''Whether or not to push the converted model and processor to the 🤗 hub.''',
  )

  __a 					=   parser.parse_args()
  convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import _LazyModule


SCREAMING_SNAKE_CASE__     :						Dict	=  {'''tokenization_wav2vec2_phoneme''': ['''Wav2Vec2PhonemeCTCTokenizer''']}


if TYPE_CHECKING:
			from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
			import sys

			SCREAMING_SNAKE_CASE__     :						Dict	=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




